基于模糊案例推理的浅隐性知识供需匹配研究

Research on Matching between Supply and Demand of Tacit Knowledge Based on Fuzzy Case-based Reasoning

隐性知识作为影响企业核心竞争能力的关键要素之一,目前在各创新主体间传递效率较低,其主要原因在于隐性知识供需主体间供需信息不匹配。基于此,为了提高隐性知识利用效率,文章着眼于浅隐性知识视角,结合其易表达特征,利用可拓物元模型表达出浅隐性知识的同时,融合案例推理方法的基本原理,设计构造了浅隐性知识的供需匹配框架。并在此基础上,利用模糊案例推理算法和信息熵,计算出浅隐性知识在供需主体之间的匹配度。结果表明:该算法通过可以实现浅隐性知识供需主体知识源的快速对接,提高浅隐性知识供需匹配效率。

Tacit knowledge,as a key factor for affecting the enterprise＇s core competition,is transformed between different innovation bodies ineffectively. The main reason is that the information of tacit knowledge is asymmetric between its supply and demand bodies. On the basis of this,to improve the utilization efficiency of tacit knowledge,this paper expresses tacit knowledge by using matter-element model from the perspective of tacit knowledge,combined with its ease expression characteristics. At the same time,the framework is designed to analyze the supply and demand matching of tacit knowledge by case-based reasoning method. Based on this,the paper calculates the matching degree between supply and demand bodies by using the fuzzy cased-reasoning algorithm and the information entropy. The result shows that this algorithm can realize the fast connection of tacit knowledge and improve the matching efficiency of tacit knowledge.